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Monocular Depth Estimation

Monocular Depth Estimation is the task of estimating the depth value (distance relative to the camera) of each pixel given a single (monocular) RGB image. This challenging task is a key prerequisite for determining scene understanding for applications such as 3D scene reconstruction, autonomous driving, and AR. State-of-the-art methods usually fall into one of two categories: designing a complex network that is powerful enough to directly regress the depth map, or splitting the input into bins or windows to reduce computational complexity. The most popular benchmarks are the KITTI and NYUv2 datasets. Models are typically evaluated using RMSE or absolute relative error.

Source: Defocus Deblurring Using Dual-Pixel Data

Papers

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Showing 151–160 of 876 papers

TitleStatusHype
Towards Robust Monocular Depth Estimation in Non-Lambertian Surfaces—0
Depth Any Canopy: Leveraging Depth Foundation Models for Canopy Height EstimationCode1
BodySLAM: A Generalized Monocular Visual SLAM Framework for Surgical ApplicationsCode1
Embodiment: Self-Supervised Depth Estimation Based on Camera Models—0
High-Precision Self-Supervised Monocular Depth Estimation with Rich-Resource Prior—0
BaseBoostDepth: Exploiting Larger Baselines For Self-supervised Monocular Depth EstimationCode1
HybridDepth: Robust Metric Depth Fusion by Leveraging Depth from Focus and Single-Image PriorsCode2
BetterDepth: Plug-and-Play Diffusion Refiner for Zero-Shot Monocular Depth Estimation—0
UMono: Physical Model Informed Hybrid CNN-Transformer Framework for Underwater Monocular Depth Estimation—0
Physical Adversarial Attack on Monocular Depth Estimation via Shape-Varying Patches—0
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